Autophagy disruption reduces mTORC1 activation leading to retinal ganglion cell neurodegeneration associated with glaucoma
Bibliographic record
Abstract
Autophagy dysfunction has been associated with several neurodegenerative diseases including glaucoma, characterized by the degeneration of retinal ganglion cells (RGCs). However, the mechanisms by which autophagy dysfunction promotes RGC damage remain unclear. Here, we hypothesized that perturbation of the autophagy pathway results in increased autophagic demand, thereby downregulating signaling through mammalian target of rapamycin complex 1 (mTORC1), a negative regulator of autophagy, contributing to the degeneration of RGCs. We identified an impairment of autophagic-lysosomal degradation and decreased mTORC1 signaling via activation of the stress sensor adenosine monophosphate-activated protein kinase (AMPK), along with subsequent neurodegeneration in RGCs differentiated from human pluripotent stem cells (hPSCs) with a glaucoma-associated variant of Optineurin (OPTN-E50K). Similarly, the microbead occlusion model of glaucoma resulting in ocular hypertension also exhibited autophagy disruption and mTORC1 downregulation. Pharmacological inhibition of mTORC1 in hPSC-derived RGCs recapitulated disease-related neurodegenerative phenotypes in otherwise healthy RGCs, while the mTOR-independent induction of autophagy reduced protein accumulation and restored neurite outgrowth in diseased OPTN-E50K RGCs. Taken together, these results highlight an important balance between autophagy and mTORC1 signaling essential for RGC homeostasis, while disruption to these pathways contributes to neurodegenerative features in glaucoma, providing a potential therapeutic target to prevent neurodegeneration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".